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Title: GROWTH OF CLOSTRIDIUM PERFRINGENS FROM SPORE INOCULA AT TEMPERATURES APPLICABLE TO COOLING OF COOKED MEAT AND POULTRY

Author
item Juneja, Vijay
item BARI, MD. LATIFUL - NFRI JAPAN
item INATSU, YASUHIRO - NFRI JAPAN
item KAWAMOTO, SHINNICHI - NFRI JAPAN

Submitted to: UJNR Food & Agricultural Panel Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 8/20/2005
Publication Date: 10/23/2005
Citation: Juneja, V.K., Bari, M., Inatsu, Y., Kawamoto, S. 2005. Growth of clostridium perfringens from spore inocula at temperatures applicable to cooling of cooked meat and poultry. UJNR Food & Agricultural Panel Proceedings.

Interpretive Summary:

Technical Abstract: Inhibition of the germination and outgrowth of Clostridium perfringens by chitosan during the abusive chilling of beef and turkey was evaluated. Chilling of cooked beef and turkey from 54.4 to 7.2 degrees C resulted in C. perfringens population increases of > 5 log10 cfu/g during 18 h exponential chill rate. However, growth of C. perfringens was completely restricted when cooked beef or turkey was supplemented with 3% chitosan and cooled from 54.4 to 7.2 degrees C in 18 h. In trypticase-peptone-glucose-yeast extract, C. perfringens growth from spores was not observed at a temperature of < 15 degrees C or > 51 degrees C for up to three weeks. We developed a model to predict C. perfringens growth from spores at temperatures applicable to the cooling of cooked meat. It was found that the use of the logistic function provided a better prediction of relative growth than the use of the Gompertz function. From the parameters of the Gompertz or logistic function the growth characteristics, germination, outgrowth and lag (GOL) times and exponential growth rate (EGR), were calculated. These growth characteristics were subsequently described by Ratkowsky functions using temperature as the independent variable. By applying multivariate statistical procedures, the standard errors and confidence intervals were computed on the predictions of relative growth for a given temperature. The predictive model should aid in evaluating the safety of cooked product after cooling and thus, with the disposition of products subject to cooling deviations.